Electric energy substitution comprehensive benefit evaluation method and system considering electricity utilization safety

Abstract

The invention provides an electric energy substitution comprehensive benefit evaluation method and system considering power utilization safety. The method comprises the steps of determining a user side index weight by adopting an analytic hierarchy process and an entropy weight method; performing dynamic grey class threshold division based on a plurality of preset grey classes; obtaining an evaluation coefficient and an evaluation proportion of each user side index under each grey class by adopting a whitening weight function; obtaining a comprehensive clustering coefficient of the electric energy substitution project under each grey class, and taking the grey class with the maximum comprehensive clustering coefficient as an evaluation result of the electric energy substitution scheme; according to the method and the system, the weight is determined through combination, and grey class evaluation calculation is performed by adopting a dynamically divided grey class threshold and a whitening weight function, so that the proportion of human factors such as expert scoring in the evaluation process is greatly reduced, subjective deviation is effectively avoided, and the objectivity and scientificity of an evaluation result are improved; and meanwhile, the dynamic nature and fuzziness of a user side index can be better adapted, so that the electric energy replacement comprehensive benefit can be reasonably evaluated.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of evaluation of electric energy substitution implementation effect, and particularly relates to a method and system for evaluating comprehensive benefits of electric energy substitution considering electric safety. BACKGROUND

[0002] Under the background of global energy transformation and increasingly urgent environmental protection requirements, in order to reduce dependence on traditional fossil energy and reduce environmental pollution, electric energy substitution projects have become an important development direction in the energy field. With the continuous progress of technology and the vigorous support of policies, electric energy substitution technologies such as electric boilers, port shore power, electric vehicles, and heat pumps have been widely popularized and applied in many fields. The application of these technologies not only improves energy utilization efficiency, but also reduces pollutant emissions, which plays a positive role in improving environmental quality and promoting sustainable economic development. At the same time, the comprehensive benefit evaluation of electric energy substitution projects has gradually attracted attention and become one of the key factors in measuring the success of projects and promoting application.

[0003] At present, the existing evaluation methods mainly focus on macro evaluation of electric energy substitution effect from the perspective of region and society. However, in actual application, there is still a lack of scientific evaluation mode for comprehensive analysis of the benefits of electric energy substitution scheme after considering user perspective and electric safety. SUMMARY

[0004] In order to overcome the shortcomings of the prior art, the present application provides a method for evaluating comprehensive benefits of electric energy substitution considering electric safety, which comprises:

[0005] Based on a plurality of user-side indexes preset in the electric energy substitution project, data sets of each user-side index are obtained, and the weights of each user-side index are determined by using the analytic hierarchy process and entropy weight method; the plurality of user-side indexes include an electric safety index;

[0006] Based on the data set of each user-side index and a plurality of preset gray classes, dynamic gray class threshold division is performed to obtain a plurality of gray class thresholds of each user-side index;

[0007] Based on the gray class thresholds of each user-side index, the evaluation coefficient and evaluation proportion of each user-side index under each gray class are obtained by using the whitening weight function;

[0008] Based on the weights of each user-side index and the evaluation proportion under each gray class, the evaluation coefficients of each user-side index under each gray class are weighted and summed to obtain the comprehensive clustering coefficient of the electric energy substitution project under each gray class, and the gray class with the maximum comprehensive clustering coefficient is taken as the evaluation result of the electric energy substitution scheme.

[0009] Preferably, the preset multiple user-side indicators in the electricity alternative project are based on, the data set of each user-side indicator is obtained, and the analytic hierarchy process and entropy weight method are used to determine the weight of each user-side indicator, including:

[0010] Based on the multiple user-side indicators preset in the electricity alternative project, the subjective weight of each user-side indicator is determined by using the analytic hierarchy process;

[0011] Obtain the original data of each user-side indicator in multiple historical periods, and perform uniformization and normalization processing on multiple original data, and combine the processed multiple original data to obtain the data set of each user-side indicator;

[0012] Based on the data set of each user-side indicator, the objective weight of each user-side indicator is determined by using the entropy weight method;

[0013] The subjective weight and objective weight of each user-side indicator are weighted and summed to obtain the weight of each user-side indicator.

[0014] Preferably, the data set of each user-side indicator and the preset multiple gray classes are based on, dynamic gray class threshold division is performed to obtain multiple gray class thresholds of each user-side indicator, including:

[0015] Based on the preset multiple gray classes, the data set of each user-side indicator is divided into dynamic gray class threshold by using clustering method to obtain multiple gray class thresholds of each user-side indicator.

[0016] Preferably, the gray class threshold of each user-side indicator is based on, the evaluation coefficient and evaluation proportion of each user-side indicator in each gray class are obtained by using the whitening weight function, including:

[0017] The whitening weight function is constructed based on the upper limit measure principle;

[0018] The data set of each user-side indicator and the gray class threshold are substituted into the whitening weight function to obtain multiple membership degrees in each gray class;

[0019] The mean value of multiple membership degrees in each gray class is calculated to obtain the evaluation coefficient of each user-side indicator in each gray class;

[0020] The evaluation coefficient of each user-side indicator in each gray class is normalized to obtain the evaluation proportion of each user-side indicator in each gray class.

[0021] Preferably, the whitening weight function satisfies the following formula:

[0022]

[0023] Wherein, f(xn ) represents a whitening function, x n represents the nth data in the data set of the user-side index, b i represents the upper limit of the grey class threshold of the grey class i, a i represents the lower limit of the grey class threshold of the grey class i.

[0024] Preferably, the calculation of the comprehensive clustering coefficient of the electric energy substitution project under each grey class satisfies the following formula:

[0025]

[0026] wherein, C i represents the comprehensive clustering coefficient of the electric energy substitution project under the grey class i, Yj is the jth user-side index, is the evaluation coefficient of the jth user-side index under the grey class i, w j is the weight of the jth user-side index, is the evaluation proportion of the jth user-side index under the grey class i, and J is the total number of user-side indexes.

[0027] Preferably, the electric energy safety index includes electrical performance, protection function and equipment operation reliability; the plurality of user-side indexes further include total investment in the base period, profit added value, government subsidy, technology popularization degree, tax added value, primary energy consumption reduction amount and carbon dioxide emission reduction amount.

[0028] Based on the same inventive concept, the present application also provides an electric energy substitution benefit evaluation system considering user-side electric energy safety, comprising:

[0029] a weight determination module configured to acquire data sets of each user-side index based on a plurality of preset user-side indexes in an electric energy substitution project, and determine the weight of each user-side index by using the analytic hierarchy process and the entropy weight method; the plurality of user-side indexes include an electric energy safety index;

[0030] a grey class division module configured to divide grey classes by using a clustering method based on the data set of each user-side index and a plurality of preset grey classes, and obtain a plurality of grey class thresholds of each user-side index;

[0031] a grey class evaluation module configured to obtain the evaluation coefficient and the evaluation proportion of each user-side index under each grey class by using a whitening function based on the grey class thresholds of each user-side index;

[0032] The evaluation module is configured to: based on the weight of each user-side index and the evaluation proportion under each gray class, weight and sum the evaluation coefficients of each user-side index under each gray class to obtain a comprehensive clustering coefficient of the electric energy substitution project under each gray class, and take the gray class with the maximum comprehensive clustering coefficient as the evaluation result of the electric energy substitution scheme.

[0033] Preferably, the weight determination module is specifically configured to:

[0034] determine the subjective weight of each user-side index based on a plurality of preset user-side indexes in the electric energy substitution project by using an analytic hierarchy process (AHP);

[0035] obtain original data of each user-side index in a plurality of historical periods, perform uniformization and normalization processing on the plurality of original data, and combine the processed plurality of original data to obtain a data set of each user-side index;

[0036] determine the objective weight of each user-side index based on the data set of each user-side index by using an entropy weight method;

[0037] weight and sum the subjective weight and the objective weight of each user-side index to obtain the weight of each user-side index.

[0038] Preferably, the gray class division module is specifically configured to:

[0039] perform dynamic gray class threshold division on the data set of each user-side index by using a clustering method based on a plurality of preset gray classes to obtain a plurality of gray class thresholds of each user-side index.

[0040] Preferably, the gray class evaluation module is specifically configured to:

[0041] construct a whitening weight function based on an upper limit measure principle;

[0042] substitute the data set of each user-side index and the gray class thresholds into the whitening weight function to obtain a plurality of membership degrees under each gray class;

[0043] calculate the mean of the plurality of membership degrees under each gray class to obtain an evaluation coefficient of each user-side index under each gray class;

[0044] perform normalization processing on the evaluation coefficient of each user-side index under each gray class to obtain an evaluation proportion of each user-side index under each gray class.

[0045] Preferably, the whitening weight function satisfies the following formula:

[0046]

[0047] wherein, f(xn ) represents the whitening function, x n represents the nth data in the data set of the user-side index, b i represents the upper limit of the grey class threshold of the grey class i, a i represents the lower limit of the grey class threshold of the grey class i.

[0048] Preferably, the calculation of the comprehensive clustering coefficient of the electric energy substitution project under each grey class satisfies the following formula:

[0049]

[0050] wherein, C i represents the comprehensive clustering coefficient of the electric energy substitution project under the grey class i, Yj is the jth user-side index, is the evaluation coefficient of the jth user-side index under the grey class i, w j is the weight of the jth user-side index, is the evaluation proportion of the jth user-side index under the grey class i, and J is the total number of user-side indexes.

[0051] Preferably, the electric energy safety index includes electrical performance, protection function and equipment operation reliability; and the plurality of user-side indexes further include total investment in the base period, profit added value, government subsidy, technology popularization degree, tax added value, primary energy consumption reduction amount and carbon dioxide emission reduction amount.

[0052] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors;

[0053] a memory for storing one or more programs;

[0054] When the one or more programs are executed by the one or more processors, a method for evaluating comprehensive benefits of electric energy substitution considering electric energy safety is realized.

[0055] Based on the same inventive concept, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed to realize a method for evaluating comprehensive benefits of electric energy substitution considering electric energy safety.

[0056] Compared with the closest prior art, the present application has the following beneficial effects:

[0057] The application provides an electric energy substitution comprehensive benefit evaluation method and system considering electric energy safety, which comprises the following steps: based on a plurality of user-side indexes preset in an electric energy substitution project, data sets of each user-side index are obtained, and the weights of each user-side index are determined by using an analytic hierarchy process and an entropy weight method; based on the data sets of each user-side index and a plurality of preset gray classes, dynamic gray class threshold division is performed to obtain a plurality of gray class thresholds of each user-side index; based on the gray class thresholds of each user-side index, a whitening weight function is used to obtain an evaluation coefficient and an evaluation proportion of each user-side index under each gray class; based on the weights of each user-side index and the evaluation proportions of each user-side index under each gray class, the evaluation coefficients of each user-side index under each gray class are weighted and summed to obtain a comprehensive clustering coefficient of the electric energy substitution project under each gray class, and the gray class with the maximum comprehensive clustering coefficient is taken as the evaluation result of the electric energy substitution scheme; the method and system determine the weights by combining the analytic hierarchy process and the entropy weight method, and perform gray class evaluation calculation by using the dynamically divided gray class thresholds and the whitening weight function, so that the proportion of human factors such as expert scoring in the evaluation process is greatly reduced, subjective bias is effectively avoided, and the objectivity, scientificity and reliability of the evaluation result are improved; at the same time, the above calculation method can better adapt to the dynamics and fuzziness of the user-side indexes, so that the electric energy substitution comprehensive benefit can be reasonably evaluated.BRIEF DESCRIPTION OF DRAWINGS DETAILED DESCRIPTION OF THE INVENTION BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 A flowchart of an electric energy substitution comprehensive benefit evaluation method considering electric energy safety is provided in the application.

[0059] Figure 2 A structure diagram of an electric energy substitution benefit evaluation system considering electric energy safety for user side is provided in the application.

[0060] Figure 3 An electronic device structure diagram is provided in the application. DETAILED DESCRIPTION

[0061] The specific embodiments of the application will be further described in detail below with reference to the accompanying drawings.

[0062] Embodiment 1

[0063] The electric energy substitution comprehensive benefit evaluation method considering electric energy safety provided by the application comprises the following steps as shown in the figure: Figure 1

[0064] S1, based on a plurality of user-side indexes preset in an electric energy substitution project, data sets of each user-side index are obtained, and the weights of each user-side index are determined by using an analytic hierarchy process and an entropy weight method; the plurality of user-side indexes comprise an electric energy safety index;

[0065] S2, based on the data set of each user-side index and the preset plurality of gray classes, dynamic gray class threshold division is performed to obtain a plurality of gray class thresholds of each user-side index;

[0066] S3, based on the gray class threshold of each user-side index, a whitening weight function is used to obtain an evaluation coefficient and an evaluation proportion of each user-side index in each gray class;

[0067] S4, based on the weight of each user-side index and the evaluation proportion in each gray class, the evaluation coefficients of each user-side index in each gray class are weighted and summed to obtain a comprehensive clustering coefficient of the electric energy substitution project in each gray class, and the gray class with the maximum comprehensive clustering coefficient is taken as the evaluation result of the electric energy substitution scheme.

[0068] The present application determines the weight by combining the analytic hierarchy process and the entropy weight method, and uses the dynamically divided gray class threshold and the whitening weight function to perform gray class evaluation calculation, which greatly reduces the proportion of human factors such as expert scoring in the evaluation process, effectively avoids subjective bias, and improves the objectivity, scientificity and reliability of the evaluation result. At the same time, the above calculation method can better adapt to the dynamics and fuzziness of the user-side index, so as to reasonably evaluate the comprehensive benefits of electric energy substitution.

[0069] It is considered that the existing electric energy substitution method mainly focuses on the macro evaluation of the three electric energy substitution effects of "electricity instead of coal", "electricity instead of oil" and "electricity instead of gas" from the regional and social angles. However, in practical application, there is still a lack of scientific evaluation mode for comprehensively analyzing the benefits of the scheme formed after the three substitutions from the micro angle and the user angle.

[0070] Insufficient micro-level analysis: The existing evaluation method often ignores the implementation details and actual effects of the electric energy substitution project at the specific user level. The needs, use scenarios and operation conditions of different users are different, and more detailed micro analysis is needed to accurately evaluate the economic benefits, environmental benefits and social benefits of the electric energy substitution scheme to individual users. For example, for small enterprise users, micro factors such as the investment cost of electric boilers, operating expenses and the influence on production processes are crucial to their decision-making, but the existing macro evaluation method is difficult to provide targeted guidance.

[0071] Lack of comprehensive analysis from the user's perspective: Users are the direct participants and beneficiaries of electric energy substitution projects, and they are more concerned about the actual impact on themselves after the implementation of the project. The current evaluation research is less from the user's perspective, and comprehensively considers factors such as cost savings, energy supply stability, equipment operation convenience, environmental improvement experience, and electricity safety. Without such comprehensive analysis, it is difficult to fully mobilize the enthusiasm of users to participate in electric energy substitution, and it is also difficult to provide comprehensive and accurate decision-making basis for users.

[0072] The analysis of benefit formation mechanism is not deep enough: For the three specific replacement schemes of "electricity instead of coal", "electricity instead of oil" and "electricity instead of gas", the formation of their benefits involves multiple links and factors, including energy conversion efficiency, equipment performance, energy price fluctuations, policy subsidies, etc. The existing research is not deep enough in comprehensively analyzing the dynamic influence of these factors on the formation of benefits, and cannot accurately predict and evaluate the long-term benefits and sustainability of different replacement schemes under different conditions.

[0073] Lack of comprehensive comparison with traditional equipment: When promoting electric energy replacement schemes, users often need to compare them with traditional equipment. The current evaluation method is not comprehensive and detailed in comparing the comprehensive performance, cost-effectiveness, environmental impact, and electricity safety of electric energy replacement schemes and traditional equipment, making it difficult for users to clearly understand the advantages and disadvantages of electric energy replacement, thereby affecting users' decision-making and the promotion speed of electric energy replacement projects.

[0074] Therefore, in order to ensure that electric energy replacement projects can operate stably and achieve sustainable development in the long term, they must have technical advantages, economic rationality, and environmental sustainability, so a technical and economic analysis must be conducted. Starting from the specific replacement measures of "electricity instead of coal", "electricity instead of oil" and "electricity instead of gas", the electric energy replacement benefit indicators, i.e. user-side indicators, are constructed under the consideration of upstream power generation pollutant emissions, the economic, social, environmental and electricity safety benefits of the three replacement methods are calculated, and they are compared with traditional equipment, thereby providing decision-making basis for energy end-users.

[0075] Specifically, all kinds of factors affecting comprehensive benefits measured for users are covered as much as possible in the research scope. According to the formation mechanism of the comprehensive benefits generated by electric energy replacement projects, the electric energy replacement comprehensive benefit evaluation indicators are divided into three categories, namely economic benefit indicators, social benefit indicators, environmental benefit indicators, and electricity safety indicators.

[0076] In this embodiment, the electricity safety indicators include electrical performance, protection function and equipment operation reliability; the multiple user-side indicators further include: total investment in the base period, profit added value, government subsidies, technology popularization degree, tax added value, primary energy consumption reduction and carbon dioxide emission reduction.

[0077] Among them, the total investment in the base period, the profit added value and the government subsidies are economic benefit indicators; the total investment in the base period is the initial investment of the project, which determines the starting scale and financial pressure; the profit added value reflects the profit growth after the implementation of the project, which measures the economic value; the government subsidies reduce the cost in various forms and encourage the implementation of the project, and the three together constitute the key elements of the economic benefit evaluation of electric energy replacement.

[0078] The three indicators in the economic benefit indicators are described as follows:

[0079] (1) Base investment total: It is a key indicator to measure the initial investment scale of the project, which directly affects the project's fund raising and financial planning. The size of the base investment total determines the project's starting scale and subsequent operating financial pressure, and is of great significance to assess the feasibility and economic risk of the project. A larger investment total may mean that the project has higher technical content and scale, but it may also bring higher financial recovery pressure; on the contrary, a smaller investment total may make it easier to start the project, but it may be limited in terms of technical upgrading and scale expansion.

[0080] (2) Profit increase value: Profit increase value directly reflects the degree of economic benefit improvement of the electric energy substitution project, and is one of the core indicators for enterprises or users to judge whether the project has economic value. Higher profit increase value means that the project can effectively increase income, reduce cost or achieve both, bringing actual economic benefits to enterprises or users, thereby promoting the further promotion and application of electric energy substitution technology. At the same time, profit increase value also has a positive impact on the financial situation and market competitiveness of enterprises, helping them expand production scale, conduct technological innovation and increase market share.

[0081] (3) Government subsidies: Government subsidies play an important role in electric energy substitution projects. For some electric energy substitution projects with large initial investment, long economic benefit return period but significant social benefits, government subsidies can alleviate the financial pressure of enterprises or users, reduce project risks and improve their enthusiasm and initiative to implement electric energy substitution. At the same time, government subsidies can also help guide market resources to the electric energy substitution field, accelerate technology research and development and popularization and application, promote industrial upgrading and the implementation of sustainable development strategy.

[0082] Technical promotion degree and tax increase value as social benefit indicators;

[0083] The two indicators in the social benefit indicators are described as follows:

[0084] (1) Technical promotion degree: Measures the spread and application range of electric energy substitution technology in user groups, reflecting its market acceptance and popularization degree, which is of great significance to promote energy transformation and technological progress, and is related to industrial development and social energy structure optimization.

[0085] (2) Tax increase value: Shows the positive contribution of electric energy substitution project implementation to tax revenue, reflecting the role of the project in economic growth and fiscal revenue, and is one of the important indicators to assess the overall economic contribution of the project to society.

[0086] Primary energy consumption reduction and carbon dioxide emission reduction as environmental benefit indicators to analyze the environmental benefits of electric energy substitution;

[0087] The environmental benefit indicators are described as follows:

[0088] (1) Primary energy consumption reduction: Primary energy (such as coal, oil, natural gas, etc.) is a limited natural resource. Reducing primary energy consumption means reducing dependence on these non-renewable resources, which helps achieve sustainable use of energy. For example, in traditional heating methods, a large amount of primary energy such as coal is used, and after electric energy is replaced (such as electric heating), the direct combustion of coal is reduced, thereby extending the service life of coal resources.

[0089] (2) Carbon dioxide emission reduction: The reduction of carbon dioxide emissions indirectly improves environmental quality. Because the traditional combustion process of primary energy not only emits carbon dioxide, but also produces other pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter, etc. Reducing carbon dioxide emissions often accompanies the reduction of these pollutants, thereby reducing air pollution, improving air quality, soil quality, and water environment quality, etc.

[0090] Electrical performance, protection function, and equipment operation reliability are used as electrical safety indicators to analyze the safety of electric energy replacement equipment;

[0091] The electrical safety indicators are described as follows:

[0092] (1) Electrical performance: A key indicator of the electrical safety of electric energy replacement equipment. It covers insulation resistance, ground resistance, leakage current, and electrical energy quality-related indicators such as voltage deviation, frequency deviation, harmonic content, voltage fluctuation, and flicker, etc. Insulation resistance reflects the integrity of the equipment insulation, and too low may cause leakage; ground resistance is related to the discharge of current in the event of a fault, and too high may increase the risk of electric shock. Leakage current directly threatens personal safety. Abnormal electrical energy quality indicators may affect the normal operation of equipment, cause faults or reduce efficiency, and the combination of these electrical performance indicators can effectively evaluate the electrical safety of equipment.

[0093] (2) Protection function: Critical to the electrical safety of electric energy replacement equipment. Short-circuit protection can quickly cut off the circuit when a short-circuit fault occurs in the equipment, preventing serious accidents such as equipment damage and fire caused by short circuits. Overload protection will automatically cut off the power or issue a warning before the device is overworked, prolonging the life of the device and reducing safety risks. Ground fault protection ensures the normal operation of the equipment grounding system and responds promptly in the event of a ground fault, ensuring the electrical safety of the equipment. A perfect protection function can effectively prevent and respond to various abnormal situations, providing strong protection for the electrical safety of the equipment.

[0094] (3) Equipment operation reliability: is an important guarantee for the safety of electric energy replacement equipment. The mean time between failures reflects the average failure-free continuous working time of the equipment in a statistical sense. The longer the mean time between failures, the higher the reliability of the equipment, and the lower the frequency of failure, thereby reducing the safety hazards caused by equipment failure. The shorter the fault repair time, the faster the equipment can recover to normal operation, reducing the impact of failure on power safety and continuity. High operation reliability equipment can operate stably for a long time, reducing maintenance costs and safety risks, and ensuring the safety and continuity of power consumption.

[0095] The above user-side indicators are all positive indicators, but considering the different types and dimensions of different indicators, the indicators are normalized and normalized in the above S1 when determining the index weight, which is convenient for subsequent weight determination. In addition, there are some indicators in the above user-side indicators, such as the degree of technical promotion, which cannot be accurately quantified, so the analytic hierarchy process is used first to determine the index weight by expert scoring. However, the weight determined by the analytic hierarchy process is more subjective and cannot improve the scientificity of the evaluation result, so the entropy weight method is used to determine the objective weight, making the calculation process of the index weight more objective and scientific.

[0096] Specifically, in the present embodiment, the above S1 can include:

[0097] Based on the plurality of user-side indicators preset in the electric energy replacement project, the subjective weight of each user-side indicator is determined by using the analytic hierarchy process;

[0098] Obtain the original data of each user-side indicator in a plurality of historical periods, and normalize and normalize the plurality of original data, and combine the plurality of original data after processing to obtain a data set of each user-side indicator;

[0099] Based on the data set of each user-side indicator, the objective weight of each user-side indicator is determined by using the entropy weight method;

[0100] The subjective weight and the objective weight of each user-side indicator are weighted and summed to obtain the weight of each user-side indicator.

[0101] Specifically, when normalizing the normalized original data, the range method can be used, or the log change method can be used for adaptive processing for skewed data.

[0102] When the subjective weight and the objective weight are weighted and summed, the subjective weight and the objective weight can be assigned a weight of 0.5. This weight can be adjusted according to user preferences. If the user pays more attention to economic indicators, the weight of the subjective weight can be increased, and if the user pays more attention to technical indicators, the weight of the objective weight can be increased, meeting the user's customization needs.

[0103] Considering that directly using expert scoring to determine the gray class threshold is too single and fixed, and cannot well reflect the different characteristics of each index, for example, the gray class threshold interval may be large due to the data dispersion of the environmental benefit index, and the gray class threshold interval may be small due to the concentrated distribution of the economic benefit index. Therefore, in the above S2, a dynamic gray class threshold division method is used to obtain the respective gray class thresholds of each user-side index.

[0104] In this embodiment, the above S2 can include:

[0105] Based on the preset multiple gray classes, a clustering method is used to perform dynamic gray class threshold division on the data set of each user-side index, to obtain multiple gray class thresholds of each user-side index.

[0106] For example, the preset multiple gray classes include excellent, good, medium, and poor, and then 5 gray class thresholds are needed to form the upper and lower limits of the first four gray classes. The upper limit of the gray class threshold of each gray class i is represented as b i , and the lower limit of the gray class threshold is represented as a i .

[0107] In this embodiment, the clustering method can specifically use a K-means clustering method or other data clustering method. The multiple historical data in the data set are clustered, and the obtained gray class thresholds can reflect the evolution of the index over time, so that the thresholds are more in line with the actual situation and have better dynamic adaptability.

[0108] In this embodiment, the above S3 can include:

[0109] A whitening weight function is constructed based on the upper limit measure principle;

[0110] The data set of each user-side index and the gray class thresholds are substituted into the whitening weight function to obtain multiple membership degrees under each gray class;

[0111] The mean value of the multiple membership degrees under each gray class is calculated to obtain an evaluation coefficient of each user-side index under each gray class;

[0112] The evaluation coefficients of each user-side index under each gray class are normalized to obtain an evaluation proportion of each user-side index under each gray class.

[0113] The evaluation coefficients of each user-side index under each gray class are normalized to obtain an evaluation proportion of each user-side index under each gray class, specifically including:

[0114] The evaluation coefficients of each user-side index under each gray class are summed to obtain a total evaluation coefficient of each user-side index;

[0115] The evaluation proportion of each user-side index in each grey class is calculated, and the evaluation proportion of each user-side index in each grey class is obtained.

[0116] In the embodiment, the whitening weight function satisfies the following formula:

[0117]

[0118] wherein f(x n ) represents the whitening weight function, x n represents the nth data in the data set of the user-side index, b i represents the upper limit of the grey class threshold of the grey class i, and a i represents the lower limit of the grey class threshold of the grey class i.

[0119] In the embodiment, the calculation process of the comprehensive clustering coefficient of the electric energy substitution project in each grey class satisfies the following formula:

[0120]

[0121] wherein C i represents the comprehensive clustering coefficient of the electric energy substitution project in the grey class i, Yj represents the jth user-side index, represents the evaluation coefficient of the jth user-side index in the grey class i, w j represents the weight of the jth user-side index, represents the evaluation proportion of the jth user-side index in the grey class i, and J represents the total number of the user-side indexes.

[0122] The present application aims to analyze the comprehensive benefits of the electric energy substitution project more completely, can effectively evaluate the comprehensive benefits of various electric energy substitution projects, and provide the suitable decision planning for the enterprises to select the appropriate electric energy substitution scheme.

[0123] Embodiment 2:

[0124] Based on the same inventive concept, the present application also provides an electric energy substitution comprehensive benefit evaluation system considering the user-side power consumption safety, as shown in Figure 2 , comprising:

[0125] a weight determination module, configured to acquire the data set of each user-side index based on the preset multiple user-side indexes in the electric energy substitution project, and determine the weight of each user-side index by using the analytic hierarchy process and the entropy weight method; the multiple user-side indexes include the power consumption safety index;

[0126] a grey class division module, configured to perform grey class division by using the clustering method based on the data set of each user-side index and the preset multiple grey classes, and obtain multiple grey class thresholds of each user-side index;

[0127] The grey class evaluation module is configured to obtain an evaluation coefficient and an evaluation proportion of each user-side index in each grey class based on respective grey class thresholds of each user-side index by using a whitening weight function;

[0128] The evaluation module is configured to obtain a comprehensive clustering coefficient of the electricity substitution project in each grey class by weighting and summing the evaluation coefficients of each user-side index in each grey class based on the weights of the user-side indexes and the evaluation proportions in the respective grey classes, and take the grey class with the largest comprehensive clustering coefficient as the evaluation result of the electricity substitution scheme.

[0129] In this embodiment, the weight determination module is specifically configured to:

[0130] determine the subjective weight of each user-side index by using an analytic hierarchy process based on the preset multiple user-side indexes in the electricity substitution project;

[0131] obtain the data set of each user-side index by combining the processed multiple original data of each user-side index in multiple historical periods;

[0132] determine the objective weight of each user-side index by using an entropy weight method based on the data set of each user-side index;

[0133] weight and sum the subjective weight and the objective weight of each user-side index to obtain the weight of each user-side index.

[0134] In this embodiment, the grey class division module is specifically configured to:

[0135] divide the data set of each user-side index by using a clustering method based on the preset multiple grey classes to obtain the multiple grey class thresholds of each user-side index.

[0136] In this embodiment, the grey class evaluation module is specifically configured to:

[0137] construct a whitening weight function based on an upper limit measure principle;

[0138] obtain multiple membership degrees in the respective grey classes by substituting the data set of each user-side index and the respective grey class thresholds into the whitening weight function;

[0139] obtain the evaluation coefficient of each user-side index in the respective grey classes by calculating the mean of the multiple membership degrees in each grey class;

[0140] obtain the evaluation proportion of each user-side index in the respective grey classes by normalizing the evaluation coefficient of each user-side index in the respective grey classes.

[0141] In this embodiment, the whitening weight function satisfies the following formula:

[0142]

[0143] Where, f(x) n ) represents the whitening weight function, x n b represents the nth data point in the dataset representing user-side metrics. i a represents the upper limit of the gray class threshold for gray class i. i This represents the lower limit of the gray class threshold for gray class i.

[0144] In this embodiment, the calculation process of the comprehensive clustering coefficient of the electricity substitution project under each gray category satisfies the following formula:

[0145]

[0146] Among them, C i Yj represents the comprehensive clustering coefficient of the electricity substitution project under gray class i, and Yj is the j-th user-side index. Let w be the evaluation coefficient of the j-th user-side indicator under gray class i. j Let j be the weight of the j-th user-side metric. The evaluation weight of the j-th user-side metric in gray class i, where J is the total number of user-side metrics.

[0147] In this embodiment, the electricity safety indicators include electrical performance, protection functions, and equipment operational reliability; the multiple user-side indicators also include: total investment in the base period, profit increase, government subsidies, technology promotion rate, tax increase, reduction in primary energy consumption, and reduction in carbon dioxide emissions.

[0148] Example 3

[0149] like Figure 3 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0150] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, and the like, which are a computing core and a control core of the terminal, and are suitable for implementing one or more instructions, and are suitable for loading and executing one or more instructions in the storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the power substitution comprehensive benefit evaluation method considering power safety in the above embodiment.

[0151] Embodiment 4

[0152] Based on the same inventive concept, the application further provides a readable storage medium, specifically an electronic device readable storage medium (Memory). The electronic device readable storage medium is a memory device in the electronic device, and is used for storing programs and data. It can be understood that the storage medium herein can include a built-in storage medium in the electronic device, and of course can also include an expansion storage medium supported by the electronic device. The storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more execution programs (including program codes). It should be noted that the storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, and the steps of the power substitution comprehensive benefit evaluation method considering user side power safety in the above embodiment can be implemented.

[0153] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product in the form of being implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0154] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0155] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0156] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0157] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit the scope of protection, although the above embodiments of the present application are described in detail, those skilled in the art should understand: the skilled person in the art can make various changes, modifications or equivalent replacements to the specific embodiments of the application after reading the present application, but these changes, modifications or equivalent replacements are all within the scope of protection of the claims of the present application.

Claims

1. A method for evaluating comprehensive benefits of electric energy substitution considering electric power safety, characterized in that, The method comprises the following steps: Based on the preset multiple user-side indicators in the electric energy replacement project, the data set of each user-side indicator is obtained, and the weights of each user-side indicator are determined by using the analytic hierarchy process and the entropy weight method; the multiple user-side indicators include the electric energy safety indicator; Based on the data set of each user-side indicator and the preset multiple gray classes, dynamic gray class threshold division is performed to obtain multiple gray class thresholds of each user-side indicator; Based on the gray class threshold of each user-side indicator, the evaluation coefficient and the evaluation proportion of each user-side indicator in each gray class are obtained by using the whitening weight function; Based on the weight of each user-side indicator and the evaluation proportion in each gray class, the evaluation coefficients of each user-side indicator in each gray class are weighted and summed to obtain the comprehensive clustering coefficient of the electric energy replacement project in each gray class, and the gray class with the maximum comprehensive clustering coefficient is taken as the evaluation result of the electric energy replacement scheme.

2. The method of claim 1, wherein, The method comprises the following steps: Based on the preset multiple user-side indicators in the electric energy replacement project, the data set of each user-side indicator is obtained, and the weights of each user-side indicator are determined by using the analytic hierarchy process and the entropy weight method; the multiple user-side indicators include the electric energy safety indicator; Based on the preset multiple user-side indicators in the electric energy replacement project, the subjective weight of each user-side indicator is determined by using the analytic hierarchy process; The original data of each user-side indicator in multiple historical periods is obtained, and the multiple original data are processed by uniformization and normalization to obtain the data set of each user-side indicator; Based on the data set of each user-side indicator, the objective weight of each user-side indicator is determined by using the entropy weight method; 3. The method of claim 2, wherein, The subjective weight and the objective weight of each user-side indicator are weighted and summed to obtain the weight of each user-side indicator. The method comprises the following steps:

4. The method of claim 2 or 3, wherein, Based on the preset multiple gray classes, the data set of each user-side indicator is divided into dynamic gray class thresholds by using the clustering method to obtain multiple gray class thresholds of each user-side indicator. The method comprises the following steps: Based on the upper limit measure principle, the whitening weight function is constructed; The data set of each user-side indicator and the gray class threshold are substituted into the whitening weight function to obtain multiple membership degrees in each gray class; The mean value of the multiple membership degrees in each gray class is calculated to obtain the evaluation coefficient of each user-side indicator in each gray class; 5. The method of claim 4, wherein, The evaluation coefficient of each user-side indicator in each gray class is normalized to obtain the evaluation proportion of each user-side indicator in each gray class. wherein f(x n ) represents a whitening weight function, x n represents the nth data in the data set of the user-side index, b i represents the upper limit of the gray class threshold of the gray class i, a i represents the lower limit of the gray class threshold of the gray class i.

6. The method of claim 1 or 2, wherein, The whitening weight function satisfies the following formula: The calculation process of the comprehensive clustering coefficient of the electric energy replacement project in each gray class satisfies the following formula: wherein C i represents the comprehensive clustering coefficient of the electric energy alternative project under the grey class i, Yj is the jth user-side index, is the evaluation coefficient of the jth user-side index under the grey class i, w j is the weight of the jth user-side index, is the evaluation proportion of the jth user-side index under the grey class i, and J is the total number of user-side indexes.

7. The method of any one of claims 1-3, wherein, The electricity safety index includes electrical performance, protection function and equipment operation reliability; the multiple user-side indexes further include: total investment in the base period, profit added value, government subsidies, technology popularization degree, tax added value, primary energy consumption reduction amount and carbon dioxide emission reduction amount.

8. A power substitution benefit evaluation system considering user-side power consumption safety, characterized in that, Comprise: A weight determination module configured to obtain a data set of each user-side index based on multiple preset user-side indexes in the electricity substitution project, and determine a weight of each user-side index by using an analytic hierarchy process and an entropy weight method; the multiple user-side indexes include an electricity safety index; A grey class division module configured to divide grey classes by using a clustering method based on the data set of each user-side index and multiple preset grey classes, to obtain multiple grey class thresholds of each user-side index; A grey class evaluation module configured to obtain an evaluation coefficient and an evaluation proportion of each user-side index under each grey class by using a whitening weight function based on the grey class thresholds of each user-side index; An evaluation module configured to obtain a comprehensive clustering coefficient of the electricity substitution project under each grey class by weighting and summing the evaluation coefficients of each user-side index under each grey class based on the weight of each user-side index and the evaluation proportion of each user-side index under each grey class, and take the grey class with the maximum comprehensive clustering coefficient as the evaluation result of the electricity substitution scheme.

9. An electronic device, comprising: Comprise: At least one processor and a memory; The memory and the processor are connected through a bus; The memory is configured to store one or more programs; When the one or more programs are executed by the at least one processor, a kind of comprehensive benefit evaluation method of electricity substitution considering electricity safety as claimed in any one of claims 1 to 7 is realized.

10. A readable storage medium, characterized by, An execution program is stored thereon, and the execution program is executed to realize a kind of comprehensive benefit evaluation method of electricity substitution considering electricity safety as claimed in any one of claims 1 to 7.

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